Run a corpus of images through multiple computer vision API vendors. View image labeing results side by side so that you can get a general feel for how well each vendor works for your use case. Supported vendors: Microsoft, IBM, Google, Cloudsight, Amazon (Rekognition) and Clarifai.
Read this blog post for details.
./input_images
.python cloudy_vision.py
./output/output.html
to see results.The keys should not be placed in the api_keys.json file but in ~/.aws/credentials and ~/.aws/config. See http://docs.aws.amazon.com/cli/latest/userguide/cli-chap-getting-started.html#cli-config-files
The keys should not be placed in the api_keys.json file but in ~/.clarifai/config. See https://github.com/Clarifai/clarifai-python#setup
You can specify tags that you hope to get, and see whether results from each vendor match. We'll compute these additional stats:
To work with tagged images, set the tagged_images
setting to True and fill a tags.json file (copy example_tags.json to get started; this file contains a map image_filename => tags
).
Install these dependencies:
virtualenv venv
source venv/bin/activate
pip install -r requirements.txt
Note that installing Pillow can be dropped if you set settings['resize']=False.
If you make modifications that may help others, please fork and send me a pull request. Some ideas for contributions: (a) add new image recognition vendors, (b) expose more attributes per vendor, e.g. face detection, (c) bugs, requests, feedback.
Authored by @goberoi.
Thanks to @lucasdchamps for several features: response time stats; matching with desired tags; Amazon Rekognition; and several other improvements.
MIT License - Copyright (c) 2016 Gaurav Oberoi.